Blind Spectral Signal Deconvolution with Sparsity Regularization: An Iteratively Reweighted Least-Squares Solution
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- 作者：Hai Liu ; Luxin Yan ; Tao Huang ; Sanya Liu…
- 关键词：Optical signal processing ; Resolution enhancement ; Spectral analysis ; Regularization ; Infrared spectroscopy
- 刊名：Circuits, Systems, and Signal Processing
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：36
- 期：1
- 页码：435-446
- 全文大小：
- 刊物类别：Engineering
- 刊物主题：Circuits and Systems; Electrical Engineering; Signal,Image and Speech Processing; Electronics and Microelectronics, Instrumentation;
- 出版者：Springer US
- ISSN：1531-5878
- 卷排序：36
文摘
Spectral signals often suffer from the common problems of band overlap and random Gaussian noise. To address these problems, we propose a sparsity regularization-based model that deconvolutes the degraded spectral signals. Sparsity regularization is achieved by fitting the probability density function of the gradient of the signal, and then, the iteratively reweighted least-squares method is used to solve the minimization problem. Results from experiments using real spectral signals showed that this algorithm separates the overlapping peaks and effectively suppresses the noise. The deconvoluted spectral signals will promote the practical application of infrared spectral analysis in the fields of target recognition, material identification, and chemometrics analysis.